Demo Data from EDBT 2026 / reviewers in the wild / expert
Raj Varma Kommaraju
dblp:59/7805
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Information theory · 67% Computational geometry · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › probability theory
probability distributions |
0.1 | 1 | 2011 | Comparing distributions and shapes using the kernel distance · SCG 2011 |
Information theory › probability theory
probability metrics |
0.1 | 1 | 2011 | Comparing distributions and shapes using the kernel distance · SCG 2011 |
Computational geometry
shape analysis |
0.1 | 1 | 2011 | Comparing distributions and shapes using the kernel distance · SCG 2011 |
Methods — techniques the papers use, named apart from their topics
kernel methods · 0.1isometric embedding · 0.1hilbert space embedding · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Comparing distributions and shapes using the kernel distanceabstractStarting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distributions over them. These distance metrics have a deep basis in functional analysis and geometric measure theory, and have a rich structure that includes an isometric embedding into a Hilbert space. They have recently been applied to numerous problems in machine learning and shape analysis. Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phillips, Suresh Venkatasubramanian |
SCG | 2 |